Report
Beverages gives strong confidence on demand in market.Beverages is less than Meat considering Europe’s statistics.Households hold the highest spot but in profits Clothes tops the list.Clothes tops the most sellable item in Cuba. Making significant important item not only in Cuba but to the entire world.---
title: "Interactive dashboards by Monisha"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
```{r,error=FALSE,warning=FALSE,message=FALSE}
library(plyr)
library(plotly)
library(ggplot2)
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(dplyr)
library(openintro)
library(highcharter)
# create some data
b <- read.csv("~/b.csv")
View(b)
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange")
```
Overview
=======================================================================
Row
-----------------------------------------------------------------------
### Consumer Items Preference Analysis
```{r,error=FALSE,warning=FALSE,message=FALSE}
valueBox(paste("Global Statistics"),
color = "warning")
```
### Total no.of.orders received during 2010-2016
```{r,error=FALSE,warning=FALSE,message=FALSE}
valueBox(length(b$Order.ID),
icon = "fa-pencil")
```
### **Total Revenue**
```{r,error=FALSE,warning=FALSE,message=FALSE}
gauge(round(mean(b$Total.Revenue),
digits = 6),
min = 0,
max = 10000000,
gaugeSectors(danger = c(0, 250000),
warning = c(250000, 2500000),
success = c(2500000, 10000000),
colors = c("red", "yellow", "green")))
```
### times Europe tops as most demanded Region
```{r,error=FALSE,warning=FALSE,message=FALSE}
valueBox(sum(b$Region == "Europe"),
icon = 'fa-tag')
```
### times Cuba tops as most demanded Country
```{r,error=FALSE,warning=FALSE,message=FALSE}
valueBox(sum(b$Country == "Cuba"),
icon = 'fa-tag')
```
Row
-----------------------------------------------------------------------
### Time-Series of purchase orders according to Region
```{r,error=FALSE,warning=FALSE,message=FALSE}
p<-plot_ly(data=b,y=~Date,color=~Region,type='scatter',mode='lines')
p
```
Data Table
=======================================================================
Row
-----------------------------------------------------------------------
```{r,error=FALSE,warning=FALSE,message=FALSE}
datatable(b,
caption = "Consumer Prefrence on Items",
rownames = T,
filter = "top",
options = list(pageLength = 25))
```
Pivot Table
=========================================
```{r,error=FALSE,warning=FALSE,message=FALSE}
rpivotTable(b,
aggregatorName = "Count",
cols= "Unit.Price",
rows = "Region",
rendererName = "Heatmap")
```
Demand
=======================================================================
Row
-----------------------------------------------------------------------
### The most sellable items are in Europe region!!
```{r,error=FALSE,warning=FALSE,message=FALSE}
x<-c('Baby Food','Beverages','Cereal','Clothes','Cosmetics','Fruits','Household','Meat','Office Supplies','Personal Care','Snacks','Vegetables')
y<-c(24,28,19,22,19,14,25,12,26,23,25,30)
data <- data.frame(x,y)
a<-ggplot(data,aes(x=x,y=y,ymin=12,ymax=30))+geom_pointrange(color="blue",size=2)+theme(axis.text.x = element_text(angle=45,hjust=1))
ggplotly(a)
```
### Which item has highest demand in market?
```{r,error=FALSE,warning=FALSE,message=FALSE}
b %>% plot_ly(labels = ~Item.Type, values = ~Units.Sold) %>% add_pie(hole = 0.6)
```
Row
-----------------------------------------------------------------------
### Which item profited the sales in the market according to priority ?
```{r,error=FALSE,warning=FALSE,message=FALSE}
p <- plot_ly(b, x = ~Item.Type, y = ~Total.Profit, color = ~Order.Priority, type = "box") %>%
layout(boxmode = "group")
p
```
### The disturibution of Total Revenue,Total Cost and Total Profit
`
```{r,error=FALSE,warning=FALSE,message=FALSE}
x<-c("TotalRevenue","TotalCost","TotalProfit")
y<-c(132,93,39)
data<-data.frame(x,y)
plot_ly(data,x=~x,y=~y,type='bar',marker=list(color=c('rgba(204,204,204,1)','rgba(204,204,204,1)', 'rgba(222,45,38,0.8)'))) %>% layout(yaxis=list(title="Measured in crores"))
```
Supply
=======================================================================
Row
-----------------------------------------------------------------------
### How Cost and Price varied during selling of Item?
```{r,error=FALSE,warning=FALSE,message=FALSE}
x<-c('Baby Food','Beverages','Cereal','Clothes','Cosmetics','Fruits','Household','Meat','Office Supplies','Personal Care','Snacks','Vegetables')
z<-c(255.28,47.45,205.7,109.28,437.2,9.33,668.27,421.89,651.21,81.73,152.58,154.06)
y<-c(159.42,31.79,117.11,35.84,263.33,6.92,502.54,364.69,524.96,56.67,97.44,90.93)
data <- data.frame(x,y,z)
data$x <- factor(data$x, levels = data[["x"]])
p <- plot_ly(data, x = ~x, y = ~y, type = 'bar', name = 'Unit Cost', marker = list(color = 'rgb(49,130,189)')) %>% add_trace(y = ~z, name = 'Unit Price', marker = list(color = 'rgb(204,204,204)')) %>%layout(xaxis = list(title = "", tickangle = -45),yaxis = list(title = ""),margin = list(b = 100),barmode = 'group')
p
```
### How the difference in Cost and Price increased Profits in market share?
```{r,error=FALSE,warning=FALSE,message=FALSE}
pal<-c("grey","grey","yellow","green","yellow","grey","grey","red","grey","grey","grey","yellow")
s<-plot_ly(data=b,x=b$Item.Type,y=((b$Unit.Price-b$Unit.Cost)/b$Unit.Price)*100,type='scatter',color=~Item.Type,colors=pal)
s
```
Row
-----------------------------------------------------------------------
### Units Sold according to Items
```{r,error=FALSE,warning=FALSE,message=FALSE}
plot_ly(b,x=~Item.Type,y=~Units.Sold,type='violin',split=~Item.Type,box=list(visible=T),mean=list(visible=T))
```
### The most sellable items are in Cuba country!!!?
```{r}
y<- c(1,1,2,1,2,1,2,1)
x<-c('Beverages','Cereal','Clothes','Cosmetics','Household','Meat','Office Supplies','Personal Care')
data <- data.frame(x,y)
plot_ly(data, labels = ~x, values =~y) %>% add_pie(hole=0.6)
```
Summary {data-orientation=columns}
===========================================
Column
-----------------------------------
### Max Unit Price
```{r,error=FALSE,warning=FALSE,message=FALSE}
valueBox(max(b$Unit.Price),
icon = "fa-random" )
```
### Max Unit Cost
```{r,error=FALSE,warning=FALSE,message=FALSE}
valueBox(max(b$Unit.Cost),
icon = "fa-random" )
```
### Average Items Pricing
```{r,error=FALSE,warning=FALSE,message=FALSE}
valueBox(round(mean(b$Unit.Price),
digits = 3),
icon = "fa-thumbs-up")
```
### Average Items Sold
```{r,error=FALSE,warning=FALSE,message=FALSE}
valueBox(round(mean(b$Units.Sold), digits = 4),
icon = "fa-thumbs-up")
```
Column
---------------------------
Report
* This is a report for 1000 Items analysed.
* Created by Monisha Anila on Consumer Preference Items Analysis.
* Europe's one of the most sellable `Beverages` gives strong confidence on demand in market.
* When coming to the overall profit `Beverages` is less than `Meat` considering Europe's statistics.
* When varying cost and price of the items `Households` hold the highest spot but in profits `Clothes` tops the list.
* The profited item `Clothes` tops the most sellable item in Cuba. Making significant important item not only in Cuba but to the entire world.
* This report was generated on `r format(Sys.Date(), format = "%B %d, %Y")`.